# Similarity Learning for Authorship Verification in Social Media

arXiv: https://arxiv.org/abs/1908.07844 · HTML: https://mlsanity.com/paper/1908.07844/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2019.

## Built on by (most cited first, 11 of 11)

- [Same Author or Just Same Topic? Towards Content-Independent Style Representations](https://mlsanity.com/paper/2204.04907/) (2022)
- [Can Large Language Models Identify Authorship?](https://mlsanity.com/paper/2403.08213/) (2024)
- [Learning Interpretable Style Embeddings via Prompting LLMs](https://mlsanity.com/paper/2305.12696/) (2023)
- [Idiosyncratic but not Arbitrary: Learning Idiolects in Online Registers Reveals Distinctive yet Consistent Individual Styles](https://mlsanity.com/paper/2109.03158/) (2021)
- [Leveraging Multilingual Training for Authorship Representation: Enhancing Generalization across Languages and Domains](https://mlsanity.com/paper/2509.16531/) (2025)
- [Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification](https://mlsanity.com/paper/2403.08462/) (2024)
- [LG4AV: Combining Language Models and Graph Neural Networks for Author Verification](https://mlsanity.com/paper/2109.01479/) (2021)
- [Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation](https://mlsanity.com/paper/2403.11265/) (2024)
- [Neurobiber: Fast and Interpretable Stylistic Feature Extraction](https://mlsanity.com/paper/2502.18590/) (2025)
- [Assessing Capabilities of Large Language Models in Social Media Analytics: A Multi-task Quest](https://mlsanity.com/paper/2604.18955/) (2026)
- [Normalisation-Based Likelihood Ratio Estimation for Forensic Authorship Verification](https://mlsanity.com/paper/2607.09501/) (2026)

## Abstract (arXiv)

Authorship verification tries to answer the question if two documents with unknown authors were written by the same author or not. A range of successful technical approaches has been proposed for this task, many of which are based on traditional linguistic features such as n-grams. These algorithms achieve good results for certain types of written documents like books and novels. Forensic authorship verification for social media, however, is a much more challenging task since messages tend to be relatively short, with a large variety of different genres and topics. At this point, traditional methods based on features like n-grams have had limited success. In this work, we propose a new neural network topology for similarity learning that significantly improves the performance on the author verification task with such challenging data sets.